For image reconstruction in CT, an iterative algorithm may include a total variation (TV) minimization. For example, some techniques have been based upon the ordered subset simultaneous algebraic reconstruction technique (OSSART), and the simultaneous algebraic reconstruction (SART). The TV minimization may also be applied to images reconstructed with an analytical algorithm such as filtered backprojection (FBP), to reduce the noise.
Despite the prior art efforts, some problems remain unsolved and require improvement. Prior art efforts generally reduce noise quite uniformly in images except for edges. In other words, although the noise may be reduced based upon a total variation (TV) minimization, the resultant image may be excessively smoothed in some regions while the other regions may be still noisy if the original image has a non-uniform distribution. On the other hand, when prior art is applied to images with subtle edges, the edges may be smoothed out. It appears that prior art total variation (TV) minimization cannot improve the noise without sacrificing spatial resolution in some cases an original image has a non-uniform noise distribution that is common in clinical applications.
Furthermore, there are some odd pixel clusters in noisy images where the HU values are 2σ to 3σ off the mean value and are far from their true values. TV minimization leaves these clusters as black/white dots in the denoised images because (1) the “cluster effect” slows down the denoising and (2) the original HU values are far from the true values.
In view of the above discussed prior art problems, a practical solution is still desired for implementing a total variation (TV) minimization algorithm to avoid the overcompensation in the low noise regions and reduce the dots in the high noise pixel clusters.